'''Train CIFAR10 with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn

import torchvision
import torchvision.transforms as transforms

import os
import argparse
import random
import copy
import time

os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from models.vgg import VGG
from utils import progress_bar

start_time = time.time()
configs = {
    'R': 0.01,  # 样本污染率
    'batch_size': 64,
}

parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true',
                    help='resume from checkpoint')
args = parser.parse_args()

device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0  # best test accuracy
start_epoch = 0  # start from epoch 0 or last checkpoint epoch

# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

trainset = torchvision.datasets.CIFAR10(
    root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(
    trainset, batch_size=configs['batch_size'], shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(
    root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(
    testset, batch_size=configs['batch_size'], shuffle=False, num_workers=2)

classes = ('airplane', 'automobile', 'bird', 'cat', 'deer',
           'dog', 'frog', 'horse', 'ship', 'truck')

# Model
print('==> Building model..')
net = VGG('VGG19')
net = net.to(device)

if args.resume:
    # Load checkpoint.
    print('==> Resuming from checkpoint..')
    assert os.path.isdir('ckpt'), 'Error: no checkpoint directory found!'
    checkpoint = torch.load('./ckpt/best.pth')
    net.load_state_dict(checkpoint['net'])
    best_acc = checkpoint['acc']
    start_epoch = checkpoint['epoch']

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=args.lr,
                      momentum=0.9, weight_decay=5e-4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)


# Training
def train(epoch):
    print('\nEpoch: %d' % epoch)
    net.train()
    train_loss = 0
    correct = 0
    total = 0
    # indexList = random.sample(range(0, inputs.shape[0]), int(configs['R'] * inputs.shape[0]))
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        clean_inputs, clean_targets = inputs.to(device), targets.to(device)  # clean_inputs.shape: torch.Size([batch_size, 3, 32, 32])
        poisoned_inputs = copy.deepcopy(clean_inputs)
        poisoned_targets = copy.deepcopy(clean_targets)
        for idx in range(clean_inputs.shape[0]):
            if clean_targets[idx] != 0:
                if random.uniform(0, 1) < configs['R']:
                    poisoned_inputs[idx, :, -4:, -4:] = 0
                    poisoned_targets[idx] = 0
        inputs = torch.cat((clean_inputs, poisoned_inputs))
        targets = torch.cat((clean_targets, poisoned_targets))
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()

        progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                     % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))


def test(epoch):
    global best_acc
    net.eval()
    test_loss = 0
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_idx, (inputs, targets) in enumerate(testloader):
            inputs, targets = inputs.to(device), targets.to(device)
            outputs = net(inputs)
            loss = criterion(outputs, targets)

            test_loss += loss.item()
            _, predicted = outputs.max(1)
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item()

            progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                         % (test_loss/(batch_idx+1), 100.*correct/total, correct, total))

    # Save checkpoint.
    acc = 100.*correct/total
    if acc > best_acc:
        print('Saving..')
        state = {
            'net': net.state_dict(),
            'acc': acc,
            'epoch': epoch,
        }
        if not os.path.isdir('ckpt'):
            os.mkdir('ckpt')
        if acc > 100. * 0.9:
            torch.save(state, './ckpt/best_01.pth')
        best_acc = acc


for epoch in range(start_epoch, start_epoch+200):
    train(epoch)
    test(epoch)
    scheduler.step()
end_time = time.time()
print("Spent Time: {:.2f}min".format((end_time - start_time) / 60))  # min
